OpenRaiser/Intern-Atlas huggingface dataset:
Action Language BC+
Venue: arXiv cs.AI 2025
Year: 2025

Action languages are formal models of parts of natural language that are designed to describe effects of actions. Many of these languages can be viewed as high level notations of answer set programs structured to represent transition systems. However, the form of answer set programs considered in the earlier work is quite limited in comparison with the modern Answer Set Programming (ASP) language, which allows several useful constructs for knowledge representation, such as choice rules, aggregates, and abstract constraint atoms. We propose a new action language called BC+, which closes the gap between action languages and the modern ASP language. The main idea is to define the semantics of BC+ in terms of general stable model semantics for propositional formulas, under which many modern ASP language constructs can be identified with shorthands for propositional formulas. Language BC+ turns out to be sufficiently expressive to encompass the best features of other action languages, such as languages B, C, C+, and BC. Computational methods available in ASP solvers are readily applicable to compute BC+, which led to an implementation of the language by extending system cplus2asp.
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
Venue: arXiv cs.LG 2025
Year: 2025

Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time series data will be available, leading also to growing demands for TSAD. While many TSAD methods already exist, new and better methods are still desirable. However, effective progress hinges on the availability of reliable means of evaluating new methods and comparing them with existing methods. We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods. Besides, all datasets and code are available at https://github.com/decisionintelligence/TAB.
Fine-Tuned Vision Transformers Capture Complex Wheat Spike Morphology for Volume Estimation from RGB Images
Venue: arXiv cs.CV 2025
Year: 2025

Estimating three-dimensional morphological traits such as volume from two-dimensional RGB images presents inherent challenges due to the loss of depth information, projection distortions, and occlusions under field conditions. In this work, we explore multiple approaches for non-destructive volume estimation of wheat spikes using RGB images and structured-light 3D scans as ground truth references. Wheat spike volume is promising for phenotyping as it shows high correlation with spike dry weight, a key component of fruiting efficiency. Accounting for the complex geometry of the spikes, we compare different neural network approaches for volume estimation from 2D images and benchmark them against two conventional baselines: a 2D area-based projection and a geometric reconstruction using axis-aligned cross-sections. Fine-tuned Vision Transformers (DINOv2 and DINOv3) with MLPs achieve the lowest MAPE of 5.08\% and 4.67\% and the highest correlation of 0.96 and 0.97 on six-view indoor images, outperforming fine-tuned CNNs (ResNet18 and ResNet50), wheat-specific backbones, and both baselines. When using frozen DINO backbones, deep-supervised LSTMs outperform MLPs, whereas after fine-tuning, improved high-level representations allow simple MLPs to outperform LSTMs. We demonstrate that object shape significantly impacts volume estimation accuracy, with irregular geometries such as wheat spikes posing greater challenges for geometric methods than for deep learning approaches. Fine-tuning DINOv3 on field-based single side-view images yields a MAPE of 8.39\% and a correlation of 0.90, providing a novel pipeline and a fast, accurate, and non-destructive approach for wheat spike volume phenotyping.
Floating-Point Data Transformation for Lossless Compression
Venue: arXiv cs.DB 2025
Year: 2025

Floating-point data is widely used across various domains. Depending on the required precision, each floating-point value can occupy several bytes. Lossless storage of this information is crucial due to its critical accuracy, as seen in applications such as medical imaging and language model weights. In these cases, data size is often significant, making lossless compression essential. Previous approaches either treat this data as raw byte streams for compression or fail to leverage all patterns within the dataset. However, because multiple bytes represent a single value and due to inherent patterns in floating-point representations, some of these bytes are correlated. To leverage this property, we propose a novel data transformation method called Typed Data Transformation (TDT) that groups related bytes together to improve compression. We implemented and tested our approach on various datasets across both CPU and GPU. TDT achieves a geometric mean compression ratio improvement of 1.16$\times$ over state-of-the-art compression tools such as zstd, while also improving both compression and decompression throughput by 1.18--3.79$\times$.
TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Driving
Venue: arXiv cs.CV 2025
Year: 2025

Multi-task learning (MTL) can advance assistive driving by exploring inter-task correlations through shared representations. However, existing methods face two critical limitations: single-modality constraints limiting comprehensive scene understanding and inefficient architectures impeding real-time deployment. This paper proposes TEM^3-Learning (Time-Efficient Multimodal Multi-task Learning), a novel framework that jointly optimizes driver emotion recognition, driver behavior recognition, traffic context recognition, and vehicle behavior recognition through a two-stage architecture. The first component, the mamba-based multi-view temporal-spatial feature extraction subnetwork (MTS-Mamba), introduces a forward-backward temporal scanning mechanism and global-local spatial attention to efficiently extract low-cost temporal-spatial features from multi-view sequential images. The second component, the MTL-based gated multimodal feature integrator (MGMI), employs task-specific multi-gating modules to adaptively highlight the most relevant modality features for each task, effectively alleviating the negative transfer problem in MTL. Evaluation on the AIDE dataset, our proposed model achieves state-of-the-art accuracy across all four tasks, maintaining a lightweight architecture with fewer than 6 million parameters and delivering an impressive 142.32 FPS inference speed. Rigorous ablation studies further validate the effectiveness of the proposed framework and the independent contributions of each module. The code is available on https://github.com/Wenzhuo-Liu/TEM3-Learning.
ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in multimodal generative models have unlocked photorealistic, instruction-aligned image generation, yet leading systems like GPT-4o-Image remain proprietary and inaccessible. To democratize these capabilities, we present ShareGPT-4o-Image, the first dataset comprising 45K text-to-image and 46K text-and-image-to-image data, all synthesized using GPT-4o's image generation capabilities for distilling its advanced image generation abilities. Leveraging this dataset, we develop Janus-4o, a multimodal large language model capable of both text-to-image and text-and-image-to-image generation. Janus-4o not only significantly improves text-to-image generation over its predecessor, Janus-Pro, but also newly supports text-and-image-to-image generation. Notably, it achieves impressive performance in text-and-image-to-image generation from scratch, using only 91K synthetic samples and 6 hours of training on an 8 A800-GPU machine. We hope the release of ShareGPT-4o-Image and Janus-4o will foster open research in photorealistic, instruction-aligned image generation.
Optimizing Resource Allocation and Energy Efficiency in Federated Fog Computing for IoT
Venue: arXiv cs.CR 2025
Year: 2025

Address Resolution Protocol (ARP) spoofing attacks severely threaten Internet of Things (IoT) networks by allowing attackers to intercept, modify, or block communications. Traditional detection methods are insufficient due to high false positives and poor adaptability. This research proposes a multi-layered machine learning-based framework for intelligently detecting ARP spoofing in IoT networks. Our approach utilizes an ensemble of classifiers organized into multiple layers, each layer optimizing detection accuracy and reducing false alarms. Experimental evaluations demonstrate significant improvements in detection accuracy (up to 97.5\%), reduced false positive rates (less than 2\%), and faster detection time compared to existing methods. Our key contributions include introducing multi-layer ensemble classifiers specifically tuned for IoT networks, systematically addressing dataset imbalance problems, introducing a dynamic feedback mechanism for classifier retraining, and validating practical applicability through extensive simulations. This research enhances security management in IoT deployments, providing robust defenses against ARP spoofing attacks and improving reliability and trust in IoT environments.
Pattern-Based Phase-Separation of Tracer and Dispersed Phase Particles in Two-Phase Defocusing Particle Tracking Velocimetry
Venue: arXiv cs.CV 2025
Year: 2025

This work investigates the feasibility of a post-processing-based approach for phase separation in defocusing particle tracking velocimetry for dispersed two-phase flows. The method enables the simultaneous 3D localization determination of both tracer particles and particles of the dispersed phase, using a single-camera setup. The distinction between phases is based on pattern differences in defocused particle images, which arise from distinct light scattering behaviors of tracer particles and bubbles or droplets. Convolutional neural networks, including Faster R-CNN and YOLOv4 variants, are trained to detect and classify particle images based on these pattern features. To generate large, labeled training datasets, a generative adversarial network based framework is introduced, allowing the generation of auto-labeled data that more closely reflects experiment-specific visual appearance. Evaluation across six datasets, comprising synthetic two-phase and real single- and two-phase flows, demonstrates high detection precision and classification accuracy (95-100%), even under domain shifts. The results confirm the viability of using CNNs for robust phase separation in disperse two-phase DPTV, particularly in scenarios where traditional wavelength-, size-, or ensemble correlation-based methods are impractical.
Automated Plan Refinement for Improving Efficiency of Robotic Layup of Composite Sheets
Venue: arXiv cs.RO 2025
Year: 2025

The automation of composite sheet layup is essential to meet the increasing demand for composite materials in various industries. However, draping plans for the robotic layup of composite sheets are not robust. A plan that works well under a certain condition does not work well in a different condition. Changes in operating conditions due to either changes in material properties or working environment may lead a draping plan to exhibit suboptimal performance. In this paper, we present a comprehensive framework aimed at refining plans based on the observed execution performance. Our framework prioritizes the minimization of uncompacted regions while simultaneously improving time efficiency. To achieve this, we integrate human expertise with data-driven decision-making to refine expert-crafted plans for diverse production environments. We conduct experiments to validate the effectiveness of our approach, revealing significant reductions in the number of corrective paths required compared to initial expert-crafted plans. Through a combination of empirical data analysis, action-effectiveness modeling, and search-based refinement, our system achieves superior time efficiency in robotic layup. Experimental results demonstrate the efficacy of our approach in optimizing the layup process, thereby advancing the state-of-the-art in composite manufacturing automation.
A phase field model for hydraulic fracture: Drucker-Prager driving force and a hybrid coupling strategy
Venue: arXiv cs.CE 2025
Year: 2025

Recent years have seen a significant interest in using phase field approaches to model hydraulic fracture, so as to optimise a process that is key to industries such as petroleum engineering, mining and geothermal energy extraction. Here, we present a novel theoretical and computational phase field framework to simulate hydraulic fracture. The framework is general and versatile, in that it allows for improved treatments of the coupling between fluid flow and the phase field, and encompasses a universal description of the fracture driving force. Among others, this allows us to bring two innovations to the phase field hydraulic fracture community: (i) a new hybrid coupling approach to handle the fracture-fluid flow interplay, offering enhanced accuracy and flexibility; and (ii) a Drucker-Prager-based strain energy decomposition, extending the simulation of hydraulic fracture to materials exhibiting asymmetric tension-compression fracture behaviour (such as shale rocks) and enabling the prediction of geomechanical phenomena such as fault reactivation and stick-slip behaviour. Four case studies are addressed to illustrate these additional modelling capabilities and bring insight into permeability coupling, cracking behaviour, and multiaxial conditions in hydraulic fracturing simulations. The codes developed are made freely available to the community and can be downloaded from {https://mechmat.web.ox.ac.uk/
Measuring Fractal Dimension using Discrete Global Grid Systems
Venue: arXiv cs.CE 2025
Year: 2025

This study builds a bridge between two well-studied but distant topics: fractal dimension and Discrete Global Grid System (DGGS). DGGSs are used as covering sets for geospatial vector data to calculate the Minkowski-Bouligand dimension. Using the method on synthetic data yields results within 1% of their theoretical fractal dimensions. A case study on opaque cloud fields obtained from satellite images gives fractal dimension in agreement with that available in the literature. The proposed method alleviates the problems of arbitrary grid placement and orientation, as well as the progression of cell sizes of the covering sets for geospatial data. Using DGGSs further ensure that intersections of the covering sets with the geospatial vector having large geographic extents are calculated by taking the curvature of the earth into account. This paper establishes the validity of DGGSs as covering sets theoretically and discusses desirable properties of DGGSs suitable for this purpose.
SoK: Current State of Ethereum's Enshrined Proposer Builder Separation
Venue: arXiv cs.CR 2025
Year: 2025

Initially introduced to Ethereum via Flashbots' MEV-boost, Proposer-Builder Separation allows proposers to auction off blockspace to a market of transaction orderers, known as builders. PBS is currently available to validators through the aforementioned MEV-boost, but its unregulated and relay-dependent nature has much of the Ethereum community calling for its enshrinement. Providing a protocol-integrated PBS marketspace and communication channel for payload outsourcing is termed PBS enshrinement. Although ePBS potentially introduces native MEV mitigation mechanisms and reduces validator operation costs, fears of multiparty collusion and chain stagnation are all too real. In addition to mitigating these potential drawbacks, PBS research pursues many tenets revered by Web3 enthusiasts, including but not limited to, censorship resistance, validator reward equity, and deflationary finance. The subsequent SoK will identify current PBS mechanisms, the need for enshrinement, additions to the ePBS upgrade, and the existing or potential on-chain socioeconomic implications of each.
Deep Learning-based Alignment Measurement in Knee Radiographs
Venue: arXiv cs.CV 2025
Year: 2025

Radiographic knee alignment (KA) measurement is important for predicting joint health and surgical outcomes after total knee replacement. Traditional methods for KA measurements are manual, time-consuming and require long-leg radiographs. This study proposes a deep learning-based method to measure KA in anteroposterior knee radiographs via automatically localized knee anatomical landmarks. Our method builds on hourglass networks and incorporates an attention gate structure to enhance robustness and focus on key anatomical features. To our knowledge, this is the first deep learning-based method to localize over 100 knee anatomical landmarks to fully outline the knee shape while integrating KA measurements on both pre-operative and post-operative images. It provides highly accurate and reliable anatomical varus/valgus KA measurements using the anatomical tibiofemoral angle, achieving mean absolute differences ~1° when compared to clinical ground truth measurements. Agreement between automated and clinical measurements was excellent pre-operatively (intra-class correlation coefficient (ICC) = 0.97) and good post-operatively (ICC = 0.86). Our findings demonstrate that KA assessment can be automated with high accuracy, creating opportunities for digitally enhanced clinical workflows.
Cross-Architecture Knowledge Distillation (KD) for Retinal Fundus Image Anomaly Detection on NVIDIA Jetson Nano
Venue: arXiv cs.CV 2025
Year: 2025

Early and accurate identification of retinal ailments is crucial for averting ocular decline; however, access to dependable diagnostic devices is not often available in low-resourced settings. This project proposes to solve that by developing a lightweight, edge-device deployable disease classifier using cross-architecture knowledge distilling. We first train a high-capacity vision transformer (ViT) teacher model, pre-trained using I-JEPA self-supervised learning, to classify fundus images into four classes: Normal, Diabetic Retinopathy, Glaucoma, and Cataract. We kept an Internet of Things (IoT) focus when compressing to a CNN-based student model for deployment in resource-limited conditions, such as the NVIDIA Jetson Nano. This was accomplished using a novel framework which included a Partitioned Cross-Attention (PCA) projector, a Group-Wise Linear (GL) projector, and a multi-view robust training method. The teacher model has 97.4 percent more parameters than the student model, with it achieving 89 percent classification with a roughly 93 percent retention of the teacher model's diagnostic performance. The retention of clinical classification behavior supports our method's initial aim: compression of the ViT while retaining accuracy. Our work serves as an example of a scalable, AI-driven triage solution for retinal disorders in under-resourced areas.
Dual-Forward Path Teacher Knowledge Distillation: Bridging the Capacity Gap Between Teacher and Student
Venue: arXiv cs.LG 2025
Year: 2025

Knowledge distillation (KD) provides an effective way to improve the performance of a student network under the guidance of pre-trained teachers. However, this approach usually brings in a large capacity gap between teacher and student networks, limiting the distillation gains. Previous methods addressing this problem either discard accurate knowledge representation or fail to dynamically adjust the transferred knowledge, which is less effective in addressing the capacity gap problem and hinders students from achieving comparable performance with the pre-trained teacher. In this work, we extend the ideology of prompt-based learning to address the capacity gap problem, and propose Dual-Forward Path Teacher Knowledge Distillation (DFPT-KD), which replaces the pre-trained teacher with a novel dual-forward path teacher to supervise the learning of student. The key to DFPT-KD is prompt-based tuning, i.e., establishing an additional prompt-based forward path within the pre-trained teacher and optimizing it with the pre-trained teacher frozen to make the transferred knowledge compatible with the representation ability of the student. Extensive experiments demonstrate that DFPT-KD leads to trained students performing better than the vanilla KD. To make the transferred knowledge better compatible with the representation abilities of the student, we further fine-tune the whole prompt-based forward path, yielding a novel distillation approach dubbed DFPT-KD+. By extensive experiments, it is shown that DFPT-KD+ improves upon DFPT-KD and achieves state-of-the-art accuracy performance.
Referring Expression Instance Retrieval and A Strong End-to-End Baseline
Venue: arXiv cs.CV 2025
Year: 2025

Using natural language to query visual information is a fundamental need in real-world applications. Text-Image Retrieval (TIR) retrieves a target image from a gallery based on an image-level description, while Referring Expression Comprehension (REC) localizes a target object within a given image using an instance-level description. However, real-world applications often present more complex demands. Users typically query an instance-level description across a large gallery and expect to receive both relevant image and the corresponding instance location. In such scenarios, TIR struggles with fine-grained descriptions and object-level localization, while REC is limited in its ability to efficiently search large galleries and lacks an effective ranking mechanism. In this paper, we introduce a new task called \textbf{Referring Expression Instance Retrieval (REIR)}, which supports both instance-level retrieval and localization based on fine-grained referring expressions. First, we propose a large-scale benchmark for REIR, named REIRCOCO, constructed by prompting advanced vision-language models to generate high-quality referring expressions for instances in the MSCOCO and RefCOCO datasets. Second, we present a baseline method, Contrastive Language-Instance Alignment with Relation Experts (CLARE), which employs a dual-stream architecture to address REIR in an end-to-end manner. Given a referring expression, the textual branch encodes it into a query embedding. The visual branch detects candidate objects and extracts their instance-level visual features. The most similar candidate to the query is selected for bounding box prediction. CLARE is first trained on object detection and REC datasets to establish initial grounding capabilities, then optimized via Contrastive Language-Instance Alignment (CLIA) for improved retrieval across images. We will release our code and benchmark publicly.
Fast Capture of Cell-Level Provenance in Numpy
Venue: arXiv cs.DB 2025
Year: 2025

Effective provenance tracking enhances reproducibility, governance, and data quality in array workflows. However, significant challenges arise in capturing this provenance, including: (1) rapidly evolving APIs, (2) diverse operation types, and (3) large-scale datasets. To address these challenges, this paper presents a prototype annotation system designed for arrays, which captures cell-level provenance specifically within the numpy library. With this prototype, we explore straightforward memory optimizations that substantially reduce annotation latency. We envision this provenance capture approach for arrays as part of a broader governance system for tracking for structured data workflows and diverse data science applications.